One of the most important tasks in microbial ecology is identification of community members and their relative abundance. Presently, microbial communities are characterized by sequencing PCR-amplified 16S rRNA molecules and analyzing the sequences computationally. Several computational pipelines exist for such analysis, and they roughly consist of removal of low-quality reads, clustering (the task of assigning a set of objects or sequences into groups, or clusters) and classification of cluster representatives.
The clustering step is considered to be essential. This is because each sequence is considered a representative of a cell, and yet, the sequencing process is inherently erroneous, and sequences with errors may be interpreted as novel organisms. Cumulatively these errors are known to inflate estimations of community richness. Therefore clustering at 97% identity is currently the common practice in the field.
However, clustering has many inherent drawbacks. Besides sequencing mistakes, clustering absorbs genuine microbial diversity. The most common sequence in the cluster is used as a representative, while other sequences in the cluster are lost. Moreover, clusters are inherently sensitive to the input data, are not stable with time and change each time new data is added. Therefore an analysis done with N samples needs to be re-clustered and therefore re-done when N+1 sample is added. Because adding samples is a frequent operation, avoiding clustering could potentially save both researcher's and computers' time.
Analysis of community composition is only one step in interrogating a microbial community. Isolation of cultures is another valuable technique. Isolates can be sequenced to identify the sequence corresponding to the reporter (amplicon) region. However, the fluid nature of clusters means that the cluster to which the isolate is assigned may shift with regards to the number of members and distribution across samples, even for the previously analyzed datasets.
Another drawback is that the current practice of assigning taxonomic classification to representative sequences of the cluster requires re-classification after each clustering. This is a potentially computationally expensive procedure. Moreover, since representative sequences in the clusters shift, taxonomy may not be consistent, further perplexing data analysis.
Sequencing technology changes every few months. The changes mostly reduce the cost of sequence per base, or increase read length. As technology changes, new data from longer amplicons is not directly comparable with legacy data. Current solutions include either using lower-resolution old data or re-sequencing old samples. Either solution has problems: first solution discards the higher resolution that new technology can provide while the second requires extensive effort of sample collection which may not be available for older samples.
Identification of members of microbial communities is an important step towards identification of microbial consortia. Microbial consortia perform many important tasks in nature, notably biodegradation of complex compounds. These consortia are typically studied in a targeted fashion, when a task in hand is selected for interrogation, the organisms of interest are identified, and the interaction studied. This case-by-case strategy allows deep understanding of some consortia, but does not present a sweeping view of variety of consortia in nature.